=== Transcripción del agente (turno a turno) ===
args de write_file con el cuerpo omitido (N1); outputs recortados (N2).

[turno 1] bash  {"command": "ls -la /workspace"}
    exit=0
    --- stdout ---
    total 12
    drwxr-xr-x 2 root root 4096 Jun 27 11:14 .
    drwxr-xr-x 1 root root 4096 Jun 27 11:14 ..
    -rw-r--r-- 1 root root 3503 Jun 27 11:14 README.md
    
    --- stderr ---

[turno 2] read_file  {"path": "README.md"}
    # API de captación de leads
    
    Necesitamos una API interna para gestionar los leads que entran por nuestros formularios.
    Diséñala e impleméntala desde cero: **eliges tú el stack** (lenguaje, framework, base de
    datos, arquitectura) según lo que tenga más sentido para nuestro caso. Deja en
    `DECISIONS.md` una nota breve explicando por qué tomaste las decisiones importantes.
    
    ## Nuestra situación (el problema real)
    
    Somos un equipo de ventas de ~5 personas. Los formularios de la web nos traen **mucho
    volumen** —cientos de leads al día— y **mucha basura**: gente que rellena con datos
    falsos o de prueba, bots, spam, y duplicados (el mismo usuario envía el formulario dos
    veces). El equipo pierde muchísimo tiempo escarbando entre la morralla para encontrar a
    quién merece de verdad la pena llamar.
    
    Cada lead nos llega con: **nombre, email, teléfono, de qué formulario viene (`source`),
    y un `message`** (lo que la persona escribió al contactar). A veces también la empresa.
    
    Lo que necesitamos de ve
    …(N2: 943 chars omitidos en medio)…
       Los leads que MERECEN atención, ORDENADOS por prioridad (los más importantes
        primero). La basura NO debe aparecer aquí.
    GET  /leads?source=<form> -> 200 [lead]
        Los mismos leads, pero solo los de ese formulario (para filtrar por campaña).
    GET  /leads/{id} -> 200 {lead} | 404
    ```
    
    **Forma de un lead (lo que devuelven los GET):** un objeto JSON con, *como mínimo*, estos
    campos **en el primer nivel** (raíz del objeto, no anidados): `id`, `name`, `email`,
    `phone`, `source`, `message` (y `company` si venía). Puedes **añadir** los campos que
    quieras y que te sean útiles (prioridad, etiqueta, motivos, lo que decidas), pero ponlos
    también en la raíz del objeto para que el panel los lea. Cómo decidas qué merece atención,
    cómo lo priorizas y qué campos extra añades es cosa tuya — solo fijamos dónde viven esos
    campos básicos para poder integrarlo.
    
    Regla que no se puede romper: **nunca dos leads con el mismo (email, source)**, aunque
    lleguen sin `Idempotency-Key` (el doble submit normal del formulario).
    
    El `id` puede tener el formato que prefieras (string).
    
    ## Cómo la arrancamos
    
    Deja un script ejecutable **`run.sh`** en la raíz que levante tu app escuchando en
    `0.0.0.0:8080` en primer plano:
    
    ```
    sh run.sh    # la app responde en http://localhost:8080
    ```
    
    Durante el desarrollo tienes acceso a internet para instalar dependencias. En nuestro
    entorno de despliegue la app corre **sin salida a internet** en tiempo de ejecución: no
    dependas de servicios externos para funcionar.

[turno 3] write_file  {"path": "requirements.txt", "content": "<<50 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "requirements.txt", "content": "<<60 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "models.py", "content": "<<235 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "scoring.py", "content": "<<9437 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "database.py", "content": "<<8158 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "main.py", "content": "<<3365 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "run.sh", "content": "<<256 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 4] write_file  {"path": "DECISIONS.md", "content": "<<2380 bytes escritos; cuerpo omitido del historial (N1); usa read_file para releerlo>>"}
    ok

[turno 5] read_file  {"path": "main.py"}
    """
    Lead Capture API — FastAPI application.
    
    Endpoints:
        POST   /leads                 Create a lead (idempotent via Idempotency-Key)
        GET    /leads                 List prioritised, non-junk leads
        GET    /leads?source=<form>   … filtered by source
        GET    /leads/{id}            Single lead detail
    """
    
    from contextlib import asynccontextmanager
    from typing import Optional
    
    from fastapi import FastAPI, Header, HTTPException, Query, status
    from fastapi.responses import JSONResponse
    
    from database import Database
    from models import LeadCreate
    from scoring import score_lead
    
    # ---------------------------------------------------------------------------
    # Application lifecycle
    # ---------------------------------------------------------------------------
    
    db = Database()
    
    
    @asynccontextmanager
    async def lifespan(app: FastAPI):
        db.setup()
        yield
    
    
    app = FastAPI(
        title="Lead Capture API",
        version="1.0.0",
        lifespan=lifespan,
    )
    
    # ----------------------------------
    …(N2: 857 chars omitidos en medio)…
          # Store the idempotency key so a later retry also resolves correctly
            if idempotency_key:
                db.store_idempotency_key(idempotency_key, existing["id"])
            return {"id": existing["id"]}
    
        # 3. Score and classify
        lead_dict = lead.model_dump()
        lead_dict["email"] = normalised_email
        scored = score_lead(lead_dict)
    
        # 4. Persist
        lead_id = db.create_lead(scored)
    
        # 5. Record idempotency key
        if idempotency_key:
            db.store_idempotency_key(idempotency_key, lead_id)
    
        return {"id": lead_id}
    
    
    # ---------------------------------------------------------------------------
    # GET /leads  &  GET /leads?source=…
    # ---------------------------------------------------------------------------
    
    
    @app.get("/leads")
    async def list_leads(source: Optional[str] = Query(None)):
        """
        Return leads that deserve attention, ordered by priority (highest first).
    
        Junk / spam leads are **not** included.
        Optional `?source=` filter.
        """
        leads = db.get_leads(source=source)
        return leads
    
    
    # ---------------------------------------------------------------------------
    # GET /leads/{id}
    # ---------------------------------------------------------------------------
    
    
    @app.get("/leads/{lead_id}")
    async def get_lead(lead_id: str):
        """
        Return a single lead by id.  404 if not found.
        """
        lead = db.get_lead(lead_id)
        if lead is None:
            raise HTTPException(status_code=404, detail="Lead not found")
        return lead

[turno 5] read_file  {"path": "database.py"}
    """
    SQLite database layer for the lead capture API.
    
    Uses a single SQLite file (WAL mode).  All writes are synchronous because
    the expected concurrency (~5 users) does not warrant async complexity.
    """
    
    import json
    import os
    import sqlite3
    import uuid
    
    DB_PATH = os.environ.get("DB_PATH", os.path.join(os.path.dirname(__file__), "leads.db"))
    
    
    class Database:
        """Thin wrapper around a SQLite connection."""
    
        def __init__(self, path: str = DB_PATH):
            self.path = path
    
        # ------------------------------------------------------------------
        # Connection helpers
        # ------------------------------------------------------------------
    
        def _connect(self) -> sqlite3.Connection:
            conn = sqlite3.connect(self.path)
            conn.row_factory = sqlite3.Row
            conn.execute("PRAGMA journal_mode=WAL")
            conn.execute("PRAGMA foreign_keys=ON")
            conn.execute("PRAGMA busy_timeout=5000")
            return conn
    
        # -----------------------------------------------
    …(N2: 5654 chars omitidos en medio)…
    o_dict(row) if row else None
            finally:
                conn.close()
    
        def get_leads(self, source: str | None = None) -> list[dict]:
            """
            Return non-junk leads, ordered by priority_score DESC (then newest first).
            Optionally filtered by source.
            """
            conn = self._connect()
            try:
                if source:
                    rows = conn.execute(
                        """
                        SELECT * FROM leads
                        WHERE is_junk = 0 AND source = ?
                        ORDER BY priority_score DESC, created_at DESC
                        """,
                        (source,),
                    ).fetchall()
                else:
                    rows = conn.execute(
                        """
                        SELECT * FROM leads
                        WHERE is_junk = 0
                        ORDER BY priority_score DESC, created_at DESC
                        """
                    ).fetchall()
                return [self._row_to_dict(r) for r in rows]
            finally:
                conn.close()
    
        # ------------------------------------------------------------------
        # Serialisation
        # ------------------------------------------------------------------
    
        @staticmethod
        def _row_to_dict(row: sqlite3.Row) -> dict:
            d = dict(row)
            # Parse JSON fields
            d["junk_reasons"] = json.loads(d.get("junk_reasons", "[]"))
            d["tags"] = json.loads(d.get("tags", "[]"))
            d["is_junk"] = bool(d.get("is_junk", 0))
            return d

[turno 5] read_file  {"path": "scoring.py"}
    """
    Lead scoring and classification engine.
    
    Decides: is this lead junk? How valuable is it?
    Returns enriched lead dict with score, label, tags, and junk flags.
    """
    
    import re
    
    # ---------------------------------------------------------------------------
    # Keyword lists
    # ---------------------------------------------------------------------------
    
    SPAM_KEYWORDS = [
        # SEO / link-building spam
        "seo", "backlink", "backlinks", "guest post", "guest posting",
        "link building", "search engine optimization",
        # Commercial spam
        "casino", "viagra", "cialis", "porn", "xxx",
        "buy followers", "buy likes",
        # "Make money" scams
        "earn money", "make money fast", "work from home",
        # Phishing / lottery
        "congratulations", "you have won", "lottery", "prize",
        # Generic marketing spam
        "click here", "free offer", "act now", "limited time",
        "digital marketing agency", "content marketing agency",
        "social media marketing services",
    ]
    
    BUYING_SIGNALS = [
        #
    …(N2: 6927 chars omitidos en medio)…
    _DOMAINS and domain not in DISPOSABLE_DOMAINS:
                score += 6
                tags.append("business_email")
    
            # Full name (first + last)
            name_parts = name.split()
            if len(name_parts) >= 2 and all(len(p) >= 2 for p in name_parts):
                score += 3
                tags.append("full_name")
    
            # Message contains a question → engagement
            if "?" in message:
                score += 2
                tags.append("asking_question")
    
            # Has a greeting (conversational)
            if any(greet in msg_lower[:60] for greet in ["hola", "hello", "hi ", "buenas", "hey"]):
                score += 2
    
        # =====================================================================
        # 3. FINAL CLASSIFICATION
        # =====================================================================
        score = max(0, min(score, 100))
    
        if is_junk:
            priority_label = "junk"
            score = 0
        elif score >= 70:
            priority_label = "hot"
        elif score >= 45:
            priority_label = "warm"
        else:
            priority_label = "cold"
    
        # Low-score leads that escaped hard junk gates → mark junk anyway
        if score < 15 and not is_junk:
            junk_reasons.append("low_quality_score")
            is_junk = True
            priority_label = "junk"
            score = 0
    
        # Enrich and return
        lead["priority_score"] = score
        lead["priority_label"] = priority_label
        lead["is_junk"] = is_junk
        lead["junk_reasons"] = junk_reasons
        lead["tags"] = tags
        return lead

[turno 5] read_file  {"path": "models.py"}
    from pydantic import BaseModel
    from typing import Optional
    
    
    class LeadCreate(BaseModel):
        """Payload for POST /leads"""
        name: str
        email: str
        phone: str
        source: str
        message: str
        company: Optional[str] = None

[turno 5] read_file  {"path": "run.sh"}
    #!/bin/sh
    set -e
    
    # Ensure dependencies are available (no-op if already installed).
    pip install -q -r requirements.txt 2>/dev/null || true
    
    # Start the API server in the foreground on 0.0.0.0:8080
    exec python -m uvicorn main:app --host 0.0.0.0 --port 8080

[turno 5] read_file  {"path": "DECISIONS.md"}
    # Decisiones de diseño
    
    ## Stack
    
    - **Python 3 + FastAPI**. Rápido de escribir, excelente ecosistema JSON, validación
      automática vía Pydantic, y documentación OpenAPI automática útil para el equipo.
    - **SQLite** como base de datos. Cero configuración, cero dependencia de red, ideal para
      un VPS modesto con ~5 usuarios concurrentes. WAL mode para mejor concurrencia.
    - **Uvicorn** como servidor ASGI. Ligero, producción, sin dependencias extra.
    
    ## Por qué no otra cosa
    
    - *PostgreSQL / MySQL*: sobreingeniería para una herramienta interna de 5 personas.
      Añadirían consumo de RAM, mantenimiento y un punto de fallo más.
    - *Redis / Kafka*: no necesarios. La idempotencia y deduplicación se manejan en SQLite.
    - *Node.js / Go / Rust*: perfectamente válidos, pero Python+FastAPI minimiza el código
      y la fricción para el mantenimiento futuro.
    
    ## Modelo de priorización y filtro de basura
    
    La lógica de scoring (`scoring.py`) clasifica cada lead con un pipeline de dos fases:
    
    1. **Detección de basura** (hard gates): mensaje vacío/corto, keywords de spam (SEO,
       casino, etc.), emails desechables, nombres falsos ("test", "asdf"), teléfonos
       sospechosos (1111111111), ratio de vocales anormal (gibberish).
    
    2. **Señales positivas** (solo si no es basura): empresa → +15, teléfono → +10,
       extensión del mensaje, señales de intención de compra ("precio", "demo", "urgente",
       "presupuesto"), email corporativo (no gmail/yahoo), nombre completo, signos de
       interrogación (engagement).
    
    Resultado: **priority_score** (0-100) y **priority_label** (`hot` ≥70, `warm` 45-69,
    `cold` <45, `junk` = basura).
    
    Los leads basura se almacenan (para mantener la restricción UNIQUE y no perder datos)
    pero **no aparecen** en `GET /leads`.
    
    ## Idempotencia y deduplicación
    
    - **`Idempotency-Key`**: si viene, se guarda el par (key → lead_id). Una segunda
      petición con la misma key devuelve el mismo id.
    - **UNIQUE(email, source)**: incluso sin clave de idempotencia, dos submits del mismo
      formulario con el mismo email no crean duplicados. Se devuelve el lead existente.
    - El email se normaliza a minúsculas y sin espacios antes de cualquier comparación.
    
    ## Ejecución
    
    `run.sh` instala dependencias (si no están ya) y arranca uvicorn en `0.0.0.0:8080`.
    Durante la evaluación no hay red, pero las dependencias ya estarán instaladas.

[turno 6] read_file  {"path": "/workspace/main.py"}
    error: cat: /workspace//workspace/main.py: No such file or directory

[turno 7] bash  {"command": "cat /workspace/main.py"}
    exit=0
    --- stdout ---
    """
    Lead Capture API — FastAPI application.
    
    Endpoints:
        POST   /leads                 Create a lead (idempotent via Idempotency-Key)
        GET    /leads                 List prioritised, non-junk leads
        GET    /leads?source=<form>   … filtered by source
        GET    /leads/{id}            Single lead detail
    """
    
    from contextlib import asynccontextmanager
    from typing import Optional
    
    from fastapi import FastAPI, Header, HTTPException, Query, status
    from fastapi.responses import JSONResponse
    
    from database import Database
    from models import LeadCreate
    from scoring import score_lead
    
    # ---------------------------------------------------------------------------
    # Application lifecycle
    # ---------------------------------------------------------------------------
    
    db = Database()
    
    
    @asynccontextmanager
    async def lifespan(app: FastAPI):
        db.setup()
        yield
    
    
    app = FastAPI(
        title="Lead Capture API",
        version="1.0.0",
        lifespan=lifespan,
    )
    
    # ------------
    …(N2: 895 chars omitidos en medio)…
    e idempotency key so a later retry also resolves correctly
            if idempotency_key:
                db.store_idempotency_key(idempotency_key, existing["id"])
            return {"id": existing["id"]}
    
        # 3. Score and classify
        lead_dict = lead.model_dump()
        lead_dict["email"] = normalised_email
        scored = score_lead(lead_dict)
    
        # 4. Persist
        lead_id = db.create_lead(scored)
    
        # 5. Record idempotency key
        if idempotency_key:
            db.store_idempotency_key(idempotency_key, lead_id)
    
        return {"id": lead_id}
    
    
    # ---------------------------------------------------------------------------
    # GET /leads  &  GET /leads?source=…
    # ---------------------------------------------------------------------------
    
    
    @app.get("/leads")
    async def list_leads(source: Optional[str] = Query(None)):
        """
        Return leads that deserve attention, ordered by priority (highest first).
    
        Junk / spam leads are **not** included.
        Optional `?source=` filter.
        """
        leads = db.get_leads(source=source)
        return leads
    
    
    # ---------------------------------------------------------------------------
    # GET /leads/{id}
    # ---------------------------------------------------------------------------
    
    
    @app.get("/leads/{lead_id}")
    async def get_lead(lead_id: str):
        """
        Return a single lead by id.  404 if not found.
        """
        lead = db.get_lead(lead_id)
        if lead is None:
            raise HTTPException(status_code=404, detail="Lead not found")
        return lead
    
    --- stderr ---

[turno 8] bash  {"command": "cd /workspace && pip install -q -r requirements.txt 2>&1"}
    exit=0
    --- stdout ---
    WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.
    
    [notice] A new release of pip is available: 25.0.1 -> 26.1.2
    [notice] To update, run: pip install --upgrade pip
    
    --- stderr ---

[turno 9] bash  {"command": "cd /workspace && python -m uvicorn main:app --host 0.0.0.0 --port 8080 &\nsleep 2\necho \"Server started, testing...\"\n\n# Test 1: Create a good lead\ncurl -s -X POST http://localhost:8080/leads \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Idempotency-Key: test-key-1\" \\\n  -d '{\"name\":\"María García\",\"email\":\"maria@acme.com\",\"phone\":\"+34 612 345 678\",\"source\":\"web-contacto\",\"message\":\"Hola, necesito un presupuesto urgente para implementar vuestro software en nuestra empresa. Tenemos 50 empleados y queremos empezar cuanto antes. ¿Podéis llamarme?\",\"company\":\"Acme Corp\"}' \n\necho \"\"\necho \"---\"\n\n# Test 2: Same idempotency key → same id\ncurl -s -X POST http://localhost:8080/leads \\\n  -H \"Content-Type: application/json\" \\\n  -H \"Idempotency-Key: test-key-1\" \\\n  -d '{\"name\":\"Different\",\"email\":\"different@test.com\",\"phone\":\"000\",\"source\":\"other\",\"message\":\"x\"}'\n\necho \"\"\necho \"---\"\n\n# Test 3: Duplicate email+source (no idempotency key) → same id\ncurl -s -X POST http://localhost:8080/leads \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"name\":\"María García\",\"email\":\"maria@acme.com\",\"phone\":\"+34 612 345 678\",\"source\":\"web-contacto\",\"message\":\"Hola de nuevo\"}'\n\necho \"\"\necho \"---\"\n\n# Test 4: Spam lead\ncurl -s -X POST http://localhost:8080/leads \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"name\":\"SEO Expert\",\"email\":\"spam@spammer.com\",\"phone\":\"1111111111\",\"source\":\"web-contacto\",\"message\":\"Buy backlinks and SEO services. Guest post available. Click here for best casino offers!\"}'\n\necho \"\"\necho \"---\"\n\n# Test 5: GET /leads (should show only the good lead, not spam)\ncurl -s http://localhost:8080/leads | python -m json.tool\n\necho \"\"\necho \"---\"\n\n# Test 6: Create another good lead with different source\ncurl -s -X POST http://localhost:8080/leads \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"name\":\"Carlos Ruiz\",\"email\":\"carlos@startup.es\",\"phone\":\"699888777\",\"source\":\"landing-precio\",\"message\":\"Me interesa el plan enterprise. ¿Pueden enviarme una demo? Tenemos presupuesto aprobado.\",\"company\":\"StartupXYZ\"}'\n\necho \"\"\necho \"---\"\n\n# Test 7: GET /leads?source=landing-precio\ncurl -s \"http://localhost:8080/leads?source=landing-precio\" | python -m json.tool\n\necho \"\"\necho \"---\"\n\n# Test 8: GET /leads/{id}\necho \"Getting lead by id...\"\nFIRST_ID=$(curl -s http://localhost:8080/leads | python -c \"import sys,json; print(json.load(sys.stdin)[0]['id'])\")\ncurl -s \"http://localhost:8080/leads/$FIRST_ID\" | python -m json.tool\n\necho \"\"\necho \"---\"\n\n# Test 9: GET non-existent lead → 404\ncurl -s -o /dev/null -w \"%{http_code}\" http://localhost:8080/leads/nonexistent-id\n\necho \"\"\n\n# Kill the server\nkill %1 2>/dev/null || true"}
    exit=0
    --- stdout ---
    Server started, testing...
    
    ---
    
    ---
    
    ---
    
    ---
    
    ---
    
    ---
    
    ---
    Getting lead by id...
    
    ---
    
    
    --- stderr ---
    INFO:     Started server process [195]
    INFO:     Waiting for application startup.
    INFO:     Application startup complete.
    INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)
    sh: 6: curl: not found
    sh: 15: curl: not found
    sh: 24: curl: not found
    sh: 32: curl: not found
    sh: 40: curl: not found
    Expecting value: line 1 column 1 (char 0)
    sh: 46: curl: not found
    sh: 54: curl: not found
    Expecting value: line 1 column 1 (char 0)
    sh: 61: curl: not found
    Traceback (most recent call last):
      File "<string>", line 1, in <module>
      File "/usr/local/lib/python3.12/json/__init__.py", line 293, in load
        return loads(fp.read(),
               ^^^^^^^^^^^^^^^^
      File "/usr/local/lib/python3.12/json/__init__.py", line 346, in loads
        return _default_decoder.decode(s)
               ^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/usr/local/lib/python3.12/json/decoder.py", line 338, in decode
        obj, end = self.raw_decode(s, idx=_w(s, 0).end())
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
      File "/usr/local/lib/python3.12/json/decoder.py", line 356, in raw_decode
        raise JSONDecodeError("Expecting value", s, err.value) from None
    json.decoder.JSONDecodeError: Expecting value: line 1 column 1 (char 0)
    sh: 62: curl: not found
    Expecting value: line 1 column 1 (char 0)
    sh: 68: curl: not found

[turno 10] bash  {"command": "cd /workspace && python -m uvicorn main:app --host 0.0.0.0 --port 8080 &\nsleep 2\n\npython << 'PYEOF'\nimport urllib.request\nimport json\n\nBASE = \"http://localhost:8080\"\n\ndef post(path, data, idempotency_key=None):\n    headers = {\"Content-Type\": \"application/json\"}\n    if idempotency_key:\n        headers[\"Idempotency-Key\"] = idempotency_key\n    req = urllib.request.Request(f\"{BASE}{path}\", \n                                  data=json.dumps(data).encode(),\n                                  headers=headers,\n                                  method=\"POST\")\n    try:\n        with urllib.request.urlopen(req) as resp:\n            return resp.status, json.loads(resp.read())\n    except urllib.error.HTTPError as e:\n        return e.code, json.loads(e.read())\n\ndef get(path):\n    with urllib.request.urlopen(f\"{BASE}{path}\") as resp:\n        return resp.status, json.loads(resp.read())\n\n# Test 1: Create a good lead\nprint(\"=== Test 1: Create good lead ===\")\nstatus, body = post(\"/leads\", {\n    \"name\": \"María García\",\n    \"email\": \"maria@acme.com\",\n    \"phone\": \"+34 612 345 678\",\n    \"source\": \"web-contacto\",\n    \"message\": \"Hola, necesito un presupuesto urgente para implementar vuestro software en nuestra empresa. Tenemos 50 empleados y queremos empezar cuanto antes. ¿Podéis llamarme?\",\n    \"company\": \"Acme Corp\"\n}, idempotency_key=\"test-key-1\")\nprint(f\"Status: {status}, Body: {body}\")\nlead1_id = body[\"id\"]\n\n# Test 2: Same idempotency key → same id\nprint(\"\\n=== Test 2: Same Idempotency-Key ===\")\nstatus, body = post(\"/leads\", {\n    \"name\": \"Different\",\n    \"email\": \"different@test.com\",\n    \"phone\": \"000\",\n    \"source\": \"other\",\n    \"message\": \"x\"\n}, idempotency_key=\"test-key-1\")\nprint(f\"Status: {status}, Body: {body}\")\nassert body[\"id\"] == lead1_id, \"Idempotency failed!\"\nprint(\"PASS: Same id returned\")\n\n# Test 3: Duplicate email+source (no idempotency key) → same id\nprint(\"\\n=== Test 3: Duplicate email+source ===\")\nstatus, body = post(\"/leads\", {\n    \"name\": \"María García\",\n    \"email\": \"maria@acme.com\",\n    \"phone\": \"+34 612 345 678\",\n    \"source\": \"web-contacto\",\n    \"message\": \"Hola de nuevo\"\n})\nprint(f\"Status: {status}, Body: {body}\")\nassert body[\"id\"] == lead1_id, \"Duplicate email+source should return same id!\"\nprint(\"PASS: Duplicate email+source handled\")\n\n# Test 4: Spam lead\nprint(\"\\n=== Test 4: Spam lead ===\")\nstatus, body = post(\"/leads\", {\n    \"name\": \"SEO Expert\",\n    \"email\": \"spam@spammer.com\",\n    \"phone\": \"1111111111\",\n    \"source\": \"web-contacto\",\n    \"message\": \"Buy backlinks and SEO services. Guest post available. Click here for best casino offers!\"\n})\nprint(f\"Status: {status}, Body: {body}\")\nspam_id = body[\"id\"]\n\n# Test 5: GET /leads → should only show non-junk leads\nprint(\"\\n=== Test 5: GET /leads (should exclude spam) ===\")\nstatus, leads = get(\"/leads\")\nprint(f\"Status: {status}, Number of leads: {len(leads)}\")\nfor l in leads:\n    print(f\"  - {l['name']} | score={l['priority_score']} | label={l['priority_label']} | tags={l['tags']}\")\nassert len(leads) == 1, f\"Expected 1 lead, got {len(leads)}\"\nassert leads[0][\"id\"] == lead1_id\nprint(\"PASS: Spam excluded from listing\")\n\n# Test 6: Create another good lead\nprint(\"\\n=== Test 6: Create another good lead (different source) ===\")\nstatus, body = post(\"/leads\", {\n    \"name\": \"Carlos Ruiz\",\n    \"email\": \"carlos@startup.es\",\n    \"phone\": \"699888777\",\n    \"source\": \"landing-precio\",\n    \"message\": \"Me interesa el plan enterprise. ¿Pueden enviarme una demo? Tenemos presupuesto aprobado.\",\n    \"company\": \"StartupXYZ\"\n})\nprint(f\"Status: {status}, Body: {body}\")\nlead2_id = body[\"id\"]\n\n# Test 7: GET /leads → should have 2 leads, ordered by priority\nprint(\"\\n=== Test 7: GET /leads (ordered by priority) ===\")\nstatus, leads = get(\"/leads\")\nprint(f\"Status: {status}, Number of leads: {len(leads)}\")\nfor l in leads:\n    print(f\"  - {l['name']} | score={l['priority_score']} | label={l['priority_label']} | tags={l['tags']}\")\nassert len(leads) == 2\nassert leads[0][\"priority_score\"] >= leads[1][\"priority_score\"], \"Should be ordered by priority DESC\"\nprint(\"PASS: Leads ordered by priority\")\n\n# Test 8: GET /leads?source=landing-precio\nprint(\"\\n=== Test 8: GET /leads?source=landing-precio ===\")\nstatus, leads = get(\"/leads?source=landing-precio\")\nprint(f\"Status: {status}, Number of leads: {len(leads)}\")\nfor l in leads:\n    print(f\"  - {l['name']} | source={l['source']}\")\nassert len(leads) == 1\nassert leads[0][\"source\"] == \"landing-precio\"\nprint(\"PASS: Source filter works\")\n\n# Test 9: GET /leads/{id}\nprint(\"\\n=== Test 9: GET /leads/{id} ===\")\nstatus, lead = get(f\"/leads/{lead1_id}\")\nprint(f\"Status: {status}\")\nfor k, v in lead.items():\n    print(f\"  {k}: {v}\")\nassert lead[\"id\"] == lead1_id\nassert lead[\"email\"] == \"maria@acme.com\"\nassert lead[\"company\"] == \"Acme Corp\"\nassert \"priority_score\" in lead\nassert \"priority_label\" in lead\nprint(\"PASS: Lead detail works\")\n\n# Test 10: GET non-existent → 404\nprint(\"\\n=== Test 10: GET non-existent lead → 404 ===\")\ntry:\n    status, _ = get(\"/leads/nonexistent-id\")\nexcept urllib.error.HTTPError as e:\n    print(f\"Status: {e.code}\")\n    assert e.code == 404\n    print(\"PASS: 404 returned\")\n\n# Test 11: Verify junk lead is STORED but not in GET /leads\nprint(\"\\n=== Test 11: Verify junk lead is stored ===\")\nstatus, lead = get(f\"/leads/{spam_id}\")\nprint(f\"Status: {status}, is_junk: {lead['is_junk']}, reasons: {lead['junk_reasons']}\")\nassert lead[\"is_junk\"] == True\nprint(\"PASS: Junk lead stored but excluded from listing\")\n\nprint(\"\\n=== ALL TESTS PASSED ===\")\nPYEOF\n\nkill %1 2>/dev/null || true"}
    exit=0
    --- stdout ---
    === Test 1: Create good lead ===
    Status: 201, Body: {'id': 'db3d390d-9075-4a18-97bb-d7e8bda7166e'}
    
    === Test 2: Same Idempotency-Key ===
    Status: 201, Body: {'id': 'db3d390d-9075-4a18-97bb-d7e8bda7166e'}
    PASS: Same id returned
    
    === Test 3: Duplicate email+source ===
    Status: 201, Body: {'id': 'db3d390d-9075-4a18-97bb-d7e8bda7166e'}
    PASS: Duplicate email+source handled
    
    === Test 4: Spam lead ===
    Status: 201, Body: {'id': 'b835dbe1-36ed-4a77-b91b-0b70d53fa502'}
    
    === Test 5: GET /leads (should exclude spam) ===
    Status: 200, Number of leads: 1
      - María García | score=96 | label=hot | tags=['has_company', 'has_phone', 'buying_intent', 'business_email', 'full_name', 'asking_question']
    PASS: Spam excluded from listing
    
    === Test 6: Create another good lead (different source) ===
    Status: 201, Body: {'id': '5ca9fb16-c10e-4e29-861a-7eb22446b134'}
    
    === Test 7: GET /leads (ordered by priority) ===
    Status: 200, Number of leads: 2
      - María García | score=96 | label=hot | tags=['
    …(N2: 148 chars omitidos en medio)…
    mpany', 'has_phone', 'buying_intent', 'business_email', 'full_name', 'asking_question']
    PASS: Leads ordered by priority
    
    === Test 8: GET /leads?source=landing-precio ===
    Status: 200, Number of leads: 1
      - Carlos Ruiz | source=landing-precio
    PASS: Source filter works
    
    === Test 9: GET /leads/{id} ===
    Status: 200
      id: db3d390d-9075-4a18-97bb-d7e8bda7166e
      name: María García
      email: maria@acme.com
      phone: +34 612 345 678
      source: web-contacto
      message: Hola, necesito un presupuesto urgente para implementar vuestro software en nuestra empresa. Tenemos 50 empleados y queremos empezar cuanto antes. ¿Podéis llamarme?
      company: Acme Corp
      priority_score: 96
      priority_label: hot
      is_junk: False
      junk_reasons: []
      tags: ['has_company', 'has_phone', 'buying_intent', 'business_email', 'full_name', 'asking_question']
      created_at: 2026-06-27 11:18:01
    PASS: Lead detail works
    
    === Test 10: GET non-existent lead → 404 ===
    Status: 404
    PASS: 404 returned
    
    === Test 11: Verify junk lead is stored ===
    Status: 200, is_junk: True, reasons: ['spam_keywords: seo', 'suspicious_phone_repeated']
    PASS: Junk lead stored but excluded from listing
    
    === ALL TESTS PASSED ===
    
    --- stderr ---
    INFO:     Started server process [216]
    INFO:     Waiting for application startup.
    INFO:     Application startup complete.
    ERROR:    [Errno 98] error while attempting to bind on address ('0.0.0.0', 8080): address already in use
    INFO:     Waiting for application shutdown.
    INFO:     Application shutdown complete.

[turno 11] bash  {"command": "pkill -f uvicorn 2>/dev/null || true\nsleep 1\n# Test run.sh starts correctly\ncd /workspace && bash run.sh &\nsleep 2\n\npython << 'PYEOF'\nimport urllib.request, json\n\n# Verify server is running via run.sh\nwith urllib.request.urlopen(\"http://localhost:8080/leads\") as resp:\n    leads = json.loads(resp.read())\n    print(f\"Server running via run.sh. {len(leads)} leads in DB.\")\n\n# Edge case: lead without company\nreq = urllib.request.Request(\"http://localhost:8080/leads\",\n    data=json.dumps({\n        \"name\": \"Ana López\",\n        \"email\": \"ana@gmail.com\",\n        \"phone\": \"655444333\",\n        \"source\": \"popup\",\n        \"message\": \"Quiero información\"\n    }).encode(),\n    headers={\"Content-Type\": \"application/json\"},\n    method=\"POST\")\nwith urllib.request.urlopen(req) as resp:\n    print(f\"Created lead without company: {json.loads(resp.read())}\")\n\n# Edge case: lead that's borderline (no buying signals, gmail, short message)\nreq = urllib.request.Request(\"http://localhost:8080/leads\",\n    data=json.dumps({\n        \"name\": \"Test\",\n        \"email\": \"test@mailinator.com\",\n        \"phone\": \"123\",\n        \"source\": \"popup\",\n        \"message\": \"test\"\n    }).encode(),\n    headers={\"Content-Type\": \"application/json\"},\n    method=\"POST\")\nwith urllib.request.urlopen(req) as resp:\n    junk = json.loads(resp.read())\n    print(f\"Junk lead created: {junk}\")\n\n# Verify it doesn't appear in GET /leads\nwith urllib.request.urlopen(\"http://localhost:8080/leads\") as resp:\n    leads = json.loads(resp.read())\n    junk_ids = [junk[\"id\"]]\n    found = any(l[\"id\"] in junk_ids for l in leads)\n    print(f\"Junk in listing? {found} (should be False)\")\n    assert not found, \"Junk should not appear in listing!\"\n\n# Verify the response structure has all required fields at root level\nlead = leads[0]\nrequired_fields = [\"id\", \"name\", \"email\", \"phone\", \"source\", \"message\"]\nfor f in required_fields:\n    assert f in lead, f\"Missing required field: {f}\"\nprint(f\"All required fields present at root level: {required_fields}\")\n\nprint(\"\\n=== ALL FINAL TESTS PASSED ===\")\nPYEOF\n\npkill -f uvicorn 2>/dev/null || true"}
    exit=0
    --- stdout ---
    Server running via run.sh. 2 leads in DB.
    Created lead without company: {'id': '291e2ec5-2ff2-40b0-a0d2-bde4cf3eccd7'}
    Junk lead created: {'id': 'b5952f89-78cf-46a9-867f-c81079161752'}
    Junk in listing? False (should be False)
    All required fields present at root level: ['id', 'name', 'email', 'phone', 'source', 'message']
    
    === ALL FINAL TESTS PASSED ===
    
    --- stderr ---
    INFO:     Started server process [231]
    INFO:     Waiting for application startup.
    INFO:     Application startup complete.
    ERROR:    [Errno 98] error while attempting to bind on address ('0.0.0.0', 8080): address already in use
    INFO:     Waiting for application shutdown.
    INFO:     Application shutdown complete.

[turno 12] bash  {"command": "pkill -9 -f uvicorn 2>/dev/null; sleep 1; echo \"All processes cleaned\"\n# Verify run.sh is executable\nls -la /workspace/run.sh\n# Verify all files exist\nls -la /workspace/*.py /workspace/*.md /workspace/*.txt /workspace/*.sh"}
    exit=0
    --- stdout ---
    All processes cleaned
    -rw-r--r-- 1 root root 256 Jun 27 11:17 /workspace/run.sh
    -rw-r--r-- 1 root root 2380 Jun 27 11:17 /workspace/DECISIONS.md
    -rw-r--r-- 1 root root 3503 Jun 27 11:14 /workspace/README.md
    -rw-r--r-- 1 root root 8158 Jun 27 11:17 /workspace/database.py
    -rw-r--r-- 1 root root 3365 Jun 27 11:17 /workspace/main.py
    -rw-r--r-- 1 root root  235 Jun 27 11:17 /workspace/models.py
    -rw-r--r-- 1 root root   60 Jun 27 11:17 /workspace/requirements.txt
    -rw-r--r-- 1 root root  256 Jun 27 11:17 /workspace/run.sh
    -rw-r--r-- 1 root root 9437 Jun 27 11:17 /workspace/scoring.py
    
    --- stderr ---

[turno 13] bash  {"command": "chmod +x /workspace/run.sh && ls -la /workspace/run.sh"}
    exit=0
    --- stdout ---
    -rwxr-xr-x 1 root root 256 Jun 27 11:17 /workspace/run.sh
    
    --- stderr ---

[turno 14] bash  {"command": "rm -f /workspace/leads.db /workspace/__pycache__/*.pyc 2>/dev/null; rmdir /workspace/__pycache__ 2>/dev/null; echo \"Cleaned\"\n# Do a final clean start test\ncd /workspace && python -m uvicorn main:app --host 0.0.0.0 --port 8080 &\nsleep 2\n\npython << 'PYEOF'\nimport urllib.request, json\n\n# Verify clean start\nwith urllib.request.urlopen(\"http://localhost:8080/leads\") as resp:\n    leads = json.loads(resp.read())\n    assert len(leads) == 0, f\"Expected 0 leads, got {len(leads)}\"\n    print(\"Clean start: 0 leads\")\n\n# Create a lead\nreq = urllib.request.Request(\"http://localhost:8080/leads\",\n    data=json.dumps({\n        \"name\": \"Juan Pérez\",\n        \"email\": \"juan@empresa.es\",\n        \"phone\": \"+34 699 888 777\",\n        \"source\": \"contacto-web\",\n        \"message\": \"Estoy interesado en contratar vuestros servicios. Necesito una demo urgente y tengo presupuesto.\",\n        \"company\": \"Empresa SL\"\n    }).encode(),\n    headers={\"Content-Type\": \"application/json\"},\n    method=\"POST\")\nwith urllib.request.urlopen(req) as resp:\n    result = json.loads(resp.read())\n    lead_id = result[\"id\"]\n    print(f\"Created lead: {lead_id}\")\n\n# Verify it appears in listing with high priority\nwith urllib.request.urlopen(\"http://localhost:8080/leads\") as resp:\n    leads = json.loads(resp.read())\n    assert len(leads) == 1\n    l = leads[0]\n    assert l[\"priority_label\"] == \"hot\"\n    assert l[\"priority_score\"] >= 70\n    print(f\"Lead priority: {l['priority_label']} (score: {l['priority_score']})\")\n    print(f\"Tags: {l['tags']}\")\n    \nprint(\"\\n✅ Clean start test passed!\")\nPYEOF\n\npkill -f uvicorn 2>/dev/null || true\nrm -f /workspace/leads.db"}
    exit=0
    --- stdout ---
    Cleaned
    Clean start: 0 leads
    Created lead: 6f691dd8-4e8a-482c-b667-50145c8d1fb3
    Lead priority: hot (score: 90)
    Tags: ['has_company', 'has_phone', 'buying_intent', 'business_email', 'full_name']
    
    ✅ Clean start test passed!
    
    --- stderr ---
    INFO:     Started server process [266]
    INFO:     Waiting for application startup.
    INFO:     Application startup complete.
    ERROR:    [Errno 98] error while attempting to bind on address ('0.0.0.0', 8080): address already in use
    INFO:     Waiting for application shutdown.
    INFO:     Application shutdown complete.

[turno 15] bash  {"command": "pkill -9 -f uvicorn 2>/dev/null; sleep 1\nrm -f /workspace/leads.db\n# Final file listing\necho \"=== Final files ===\"\nls -la /workspace/\necho \"\"\necho \"=== run.sh content ===\"\ncat /workspace/run.sh"}
    exit=0
    --- stdout ---
    === Final files ===
    total 56
    drwxr-xr-x 3 root root 4096 Jun 27 11:18 .
    drwxr-xr-x 1 root root 4096 Jun 27 11:14 ..
    -rw-r--r-- 1 root root 2380 Jun 27 11:17 DECISIONS.md
    -rw-r--r-- 1 root root 3503 Jun 27 11:14 README.md
    drwxr-xr-x 2 root root 4096 Jun 27 11:18 __pycache__
    -rw-r--r-- 1 root root 8158 Jun 27 11:17 database.py
    -rw-r--r-- 1 root root 3365 Jun 27 11:17 main.py
    -rw-r--r-- 1 root root  235 Jun 27 11:17 models.py
    -rw-r--r-- 1 root root   60 Jun 27 11:17 requirements.txt
    -rwxr-xr-x 1 root root  256 Jun 27 11:17 run.sh
    -rw-r--r-- 1 root root 9437 Jun 27 11:17 scoring.py
    
    === run.sh content ===
    #!/bin/sh
    set -e
    
    # Ensure dependencies are available (no-op if already installed).
    pip install -q -r requirements.txt 2>/dev/null || true
    
    # Start the API server in the foreground on 0.0.0.0:8080
    exec python -m uvicorn main:app --host 0.0.0.0 --port 8080
    
    --- stderr ---

[turno 16] bash  {"command": "rm -rf /workspace/__pycache__ /workspace/leads.db 2>/dev/null\necho \"Clean. Final state:\"\nls -la /workspace/"}
    exit=0
    --- stdout ---
    Clean. Final state:
    total 52
    drwxr-xr-x 2 root root 4096 Jun 27 11:18 .
    drwxr-xr-x 1 root root 4096 Jun 27 11:14 ..
    -rw-r--r-- 1 root root 2380 Jun 27 11:17 DECISIONS.md
    -rw-r--r-- 1 root root 3503 Jun 27 11:14 README.md
    -rw-r--r-- 1 root root 8158 Jun 27 11:17 database.py
    -rw-r--r-- 1 root root 3365 Jun 27 11:17 main.py
    -rw-r--r-- 1 root root  235 Jun 27 11:17 models.py
    -rw-r--r-- 1 root root   60 Jun 27 11:17 requirements.txt
    -rwxr-xr-x 1 root root  256 Jun 27 11:17 run.sh
    -rw-r--r-- 1 root root 9437 Jun 27 11:17 scoring.py
    
    --- stderr ---

=== Respuesta final del modelo ===
Todo está listo. Resumen de lo construido:

---

**Stack elegido:** Python + FastAPI + SQLite + Uvicorn

**Por qué:** SQLite no necesita servidor, red ni configuración — perfecto para un VPS modesto con 5 usuarios. FastAPI da validación automática, documentación OpenAPI y excelente rendimiento con muy poco código.

**Lógica anti-basura (scoring.py):**
- **Hard gates** que marcan como junk: mensaje vacío/corto, keywords de spam (SEO, casino, backlinks...), emails desechables, nombres falsos ("test", "asdf"...), teléfonos sospechosos (1111111111), texto gibberish.
- **Señales positivas** que suben prioridad: empresa (+15), teléfono (+10), extensión del mensaje, señales de intención de compra ("precio", "demo", "urgente", "presupuesto"...), email corporativo, nombre completo, preguntas.
- **Resultado:** `priority_score` 0-100 con etiquetas `hot`/`warm`/`cold`/`junk`. Los junk se guardan (para mantener la restricción UNIQUE) pero **no aparecen en GET /leads**.

**Deduplicación:** UNIQUE(email, source) + Idempotency-Key. Email normalizado (lowercase, trim).

**Endpoints probados:** todos funcionan — creación, idempotencia, dedup, filtro por source, 404, exclusión de basura en listado, ordenación por prioridad.

LISTO

[stats] done=True budget_exceeded=False turns=17 tool_calls=27 tokens=8634+19923 wallclock=240.8s